1. Field of the Invention
The present invention relates to the field of task selection. More specifically, the present invention relates to the on-line selection of tasks such that a desired distribution of these tasks is followed.
2. Related Art
An information record typically contains a multiplicity of variables (or attributes and/or fields), with information preferably provided for each variable in the record. Based on the information in the record, the record can be classified (segmented) into one or more of a number of different categories.
For example, the variables in a customer record might include the customer's level of education, income, address, hobbies and interests, and recent purchases. The customer is commonly requested to provide this type of information on product registration cards or warranty cards provided to the customer when he or she purchases a product. Additionally, this type of information is also frequently requested from customers when they shop on-line (e.g., over the Internet). Certain information can also be obtained from the customer's computer upon connecting to a web site over the Internet. Further, marketing surveys are also performed in order to deliberately gather such information.
A large amount of information and data is generated using these approaches, given the large number of responses, the long list of requested information, and the diversity of the responses. To bring order to the data, classification tools are used to categorize (or classify or segment) each customer record based on the information it contains. In this way a company's customer base can be further categorized into various segments. Each of the segments are associated with an independent set of characteristics or rules that generally describe the customers, old or new, that fall within the segment. For example, a shoe company may segment their customers using various characteristics (e.g., shoe size, age of customer, sex of customer, activities participated by the customer, income of the customer, how much does the customer spend on a regular basis, how has the customer reacted to promotions in the past, etc.).
In one instance, once a company decides on how to segment their customer base, an advertising campaign can be tailored to take advantage of the segmented information. In the simplest terms, an advertising campaign may have numerous advertising promotions that can be offered to customers of a particular segment. An advertising campaign can be expanded to include numerous segments, each of which are targeted with advertising promotions. Advertising promotions for one segment may be the same, different, similar as the advertising promotions offered to other segments. Additionally, an advertising promotion may include an offer for sale of a product, a coupon for a product, a rebate on a product, etc.
Depending on a campaign's objective, an optimized distribution of the advertising promotions is created for each segment of the customer base. This distribution describes a desired distribution of all the advertising promotions offered to customers in a particular segment. The desired distribution is designed to achieve a particular objective (e.g., maximizing profit, generating revenue, reducing inventory, gaining new customers, etc.).
Implementation of the advertising campaign can occur through various mediums. One traditional medium is through the mail. For example, promotions could be distributed to the various customers in a segment by mailing flyers, coupons, rebates, etc. A new medium is the Internet, or any other suitable communication network. One benefit to using the Internet for implementing an advertising campaign is quicker exposure and turn-around time. For example, a customer to a web site can be immediately presented with an advertising promotion. Moreover, the customer can immediately react to the promotion by instantly purchasing the promoted item.
Whatever the medium used to implement the advertising campaign, in order to fully achieve the objectives of the campaign, the actual distribution of advertising campaigns must be as close as possible to the desired distribution as designed. Ideally, this mirrored distribution must occur at any point in time during the advertising campaign.
However, previous methods for implementing a desired distribution do not adequately distribute an advertising campaign in an Internet environment. Although these previous methods are adequate in a mailing environment, these methods do not adequately distribute advertising promotions to match desired distributions where the amount of customers arriving at a web site and the frequency of customers arriving are not well known.
FIG. 1 illustrates an generalized advertising campaign that may be implemented in the prior art. FIG. 1 shows a matrix table 100 of a simplified advertising campaign. The campaign is targeted towards two segments of customers, segment-A 110 and segment-B 120. Advertising promotions are offered to each segment in varying proportions. In this case, each of the advertising promotions are offered to both segments 110 and 120.
The promotions offered to the segments are promotion-1 132, promotion-2 134, and promotion-3 136. In an advertising campaign tailored towards selling shoes, promotion-1 132 could be an offer for black running shoes, for example. Likewise, promotion-2 134 could be an offer for white basketball shoes. Also, promotion-3 136 could be an offer for black basketball shoes.
For each segment, an optimized distribution was created. Looking at the row for segment-A 110, the distribution of promotions was optimized such that thirty percent of all offers for promotions made to the customers of segment-A 110 would be for promotion-1 132. Likewise, twenty percent of all offers for promotions made to the customers of segment-A 110 would be for promotion-2 134. Also, fifty percent of all offers for promotions made for customers of segment-A 110 would be for promotion-3 136.
Previously, various methods for implementing the advertising promotions included random allocation, naive round robin, and general round robin. However, each of these methods are inadequate in an environment where the number of customers are unknown and where the frequency of the customers are unknown. This is especially the case when an advertising campaign is implemented over a communication network, such as the Internet.
In the case of random allocation, pseudo random numbers are utilized to achieve the desired or desired distribution. In the above example, advertising promotions for a particular customer are picked from the list of all advertising promotions with a probability equal to the proportion required. For example, for a customer in segment-A 110, there would be a thirty percent chance that the customer would receive promotion-1 132, a twenty percent chance for promotion-2 134, and a fifty percent chance for receiving promotion-3 136. However, since random allocation is a random scheme, there is a chance that realized or actual proportions are far off from the required ones, especially when the sample size of customers is small.
In the case of naive and generalized round robin, a period is chosen. Calculations are then made-to determine how many repetitions of each advertising promotion is necessary per period such that the distribution of advertising promotions made within the period match the desired distribution. This period is then repeated continuously until the advertising campaign is completed. For example, in segment-A, picking a period of ten, promotion-1 132 must be offered three times, promotion-2 134 must be offered twice, and promotion-3 136 must be offered five times.
In the case of naive round robin, for every period, the advertising promotions that are offered and their repetitions are performed in sequence. For example, for segment-A 110, for a period of ten customers being made offers, the sequence would be as follows: 1112233333, or promotion-1 132 three times, then promotion-2 134 twice, and then promotion-3 136 five times. This sequence is repeated continuously until the completion of the advertising campaign.
General round-robin is similar to the naive round-robin. However, the order in which the advertising promotions are offered within a period are changed to more closely resemble the desired distribution at points within the period. For example, for segment-A 110, for a period of ten customers being made offers, the sequence could be as follows: 1233123313, or promotion-1, promotion-2, promotion-3 twice, promotion-1, promotion-2, promotion-3 twice, promotion-1, and promotion-3.
While easy to compute, the round-robin approach, naive or general, has the disadvantage that especially for small samples, the desired distribution and the actual distribution can be quite different. This is especially the case when an advertising campaign ends before the completion of period.
Furthermore, for general proportions, the period length required to achieve the desired distribution might be substantial. For example, in segment-B 120, the lowest value for a period that exactly achieves the desired distribution is one-hundred samples, since the distribution is expressed in whole numbers: thirty-one percent for promotion-1 132, twenty-seven percent for promotion-2 134, and fifty-two percent for promotion-3 136. This would increase the chance of an advertising campaign ending within the middle of a period of distribution and not achieving the desired distribution. Also, the larger the period, the more difficult it is to calculate the specific distribution sequence for a particular period.
Moreover, if the distribution percentages were more tailored to a specific objective, such as by not rounding up the percentages, the period would further increase. For example, in Segment-C 140, the lowest value for a period that exactly achieves the desired distribution is one-thousand samples, since the distribution is not expressed in whole numbers: 25.3% for promotion-1 132, 33.4% for promotion-2 134, and 41.3% for promotion-3 136. Again, by increasing the period, especially for small samples, the actual distribution has a higher chance of being off from the desired distribution. This problem is accentuated the more finely tuned or accurate the distribution percentages are.
Thus, the use of random allocation, naive round-robin, and general round-robin to achieve distributions close to desired distribution patterns are inadequate in environments where the amount of customers and the frequency of customers are unknown.